Indoor Received Power Measurements Associated with Reference Transmitter Location using Wireless Sensor Network

Citation Author(s):
Ivo
Bizon
Technische Universität Dresden
Zhongju
Li
Technische Universität Dresden
Friedrich
Burmeister
Technische Universität Dresden
Submitted by:
Ivo Bizon F. de...
Last updated:
Mon, 08/29/2022 - 04:18
DOI:
10.21227/c8ct-gs89
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Abstract 

Accurately obtaining the position of active transmitters within an indoor wireless network has promising applications in future wireless networks. However, due to the complex propagation phenomena experienced by signals indoors, classical model-based localization techniques present poor accuracy, and machine learning (ML) based positioning has a promising potential to deliver high accuracy localization services indoors. Hence, datasets containing real-world measurands available represent an important step to better understand the achievable performance of ML-based positioning schemes.

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Funding Agency: 
European Union Horizon 2020, Deutsche Forschungsgemeinschaft (DFG), German Federal Ministry of Education and Research (BMBF)
Grant Number: 
957216, 390696704, 16KISK001K